Mileage and speed estimation

ABSTRACT

An approach to determining vehicle usage makes use of a sensor that provides a vibration signal associated with the vehicle, and that vibration signal is used to infer usage. Usage can include distance traveled, optionally associated with particular ranges of speed or road type. In a calibration phase, auxiliary measurements, for instance based on GPS signals, are used to determine a relationship between the vibration signal and usage. In a monitoring phase, the determined relationship is used to infer usage from the vibration signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority to, and for U.S. purposes is aContinuation-in-Part (CIP) of, U.S. application Ser. No. 15/211,478,filed on Jul. 15, 2016, the contents of which are incorporated herein byreference.

BACKGROUND

Insurance companies have a great interest in predicting the claims costof an insured vehicle. The mileage of the vehicle is considered a usefulpredictor of claims cost, and is commonly estimated through odometerreadings. The duration of time spent traveling at unsafe speeds can alsobe useful for predicting claims cost, but it is infrequently recorded.

The current methods for estimating mileage and speed suffer from severaldefects. One approach is to install a device into an On-BoardDiagnostics (OBD) port on a vehicle thereby enabling direct acquisitionof odometer and speedometer readings continuously. However, such OBDdevices are prone to accidental removal, can drain the vehicle'sbattery, and can be costly to build and operate.

Furthermore, use of a vehicle's odometer or speedometer can result insignificant error. For example, overestimates of mileage based on anodometer can be 5-7% too high. Furthermore, an error or bias in anodometer reading results is a progressively greater divergence of themeasured mileage from the distance actually traveled. There is no legalmandate for odometer accuracy in the USA. The European regulation isECE-R 39, which mandates ±10% accuracy; a proposal to improve this to±4% failed as “not practically feasible”. Speedometers are relativelymore accurate, but tend to overestimate speed, resulting in significanterror in estimated usage of a vehicle using the speedometer reading.

GPS-enabled smartphones typically provide a more accurate measurement ofdistance and an estimate of speed, but require that the smartphone bepresent and operating consuming power. The Applicant's prior patent,U.S. Pat. No. 9,228,836, titled “Inference of vehicular trajectorycharacteristics with personal mobile devices,” issued on Jan. 5, 2016,incorporated herein by reference, describes an approach which does notrely solely on GPS in which a user's smartphone may be configured with asoftware application to accurately determine longitudinal accelerationand lateral acceleration of a vehicle and infer vehicle velocity andcorresponding distance traveled by processing raw data from anaccelerometer in the smarphone, which may be oriented arbitrarily in amoving vehicle (or carried by a moving user), and whose orientation andposition may change arbitrarily during the motion. Although such asoftware application may be used to record a driver's vehicle usage, forexample, to determine the distance traveled, speed, or other drivingcharacteristics, there are times when the driver may not have theirsmartphone with them, or the software application may not be executing.

SUMMARY

In one aspect, in general, a vehicle-installed device senses vibrationin a vehicle, and based on this sensed vibration, determines usage ofthe vehicle. For example, the device may be in the form of a smallbattery-powered “tag” that is affixed to the windshield of the vehicle,and that can communicate in a wireless manner with a user's smartphone.One use of the device is to augment a smartphone-based approach, such asdescribed in U.S. Pat. No. 9,228,836, for tracking vehicle usage suchthat after a period of usage when the smartphone has not been present oractive. The next time the smartphone is present in the car, the devicetransmits recorded information to the smartphone. When the smartphonehas network access, the phone in turn transmits the data to a serversystem. This data is used to infer vehicle usage during the period thatthe phone was absent. In this way, a more complete record of a vehicle'susage may be obtained.

In another aspect, in general, an approach to determining vehicle usagemakes use of a sensor that provides a vibration signal associated withtravel by the vehicle, and that vibration signal is used to infer usage.Usage can include distance traveled, optionally associated withparticular ranges of speed or road type. Vibration should be understoodbroadly to include any motion-based phenomenon, for example, relating toposition, velocity, or acceleration of a part of the vehicle (e.g, theframe, a suspension member, etc.), with the vibration signal beingrepresented in the time domain or in the frequency domain (e.g.,intensity at one or more frequencies or over a range of frequencies). Insome embodiments, in a “calibration phase,” auxiliary measurements, forinstance based on GPS signals or other positioning approaches, are usedto determine a relationship between the vibration signal and usage. In a“monitoring phase,” the determined relationship is used to infer usagefrom the vibration signal.

Not all travel is equally dangerous. For example, a mile driven at nightis more dangerous than a mile driven during the day. Similarly, the riskof a mile driven on a highway may differ from the risk of a mile drivenon a surface street. With that in mind, we may wish to distinguish themileage according to mode of use. For instance, the tag may determinethat during one trip, the vehicle has driven 12 miles on the highway and7 miles on surface streets.

In another aspect, in general, a method for determining usage of avehicle comprises acquiring a vibration signal from a sensor of a devicetraveling with the vehicle. The vibration signal is processed in thedevice to determine a characteristic of the signal related to a use ofthe vehicle. The determined characteristic is then used to identify timeperiods during which the vehicle is in a first mode of use (forinstance, in a first mode of travel). Usage is accumulated in a datastorage in the device. This includes accumulating a first usage for thevehicle during the identified time periods in which the vehicle is inthe first mode of use. The accumulated usage is transmitted from thedevice.

Aspects can include one or more of the following features.

The first mode of use corresponds to a mode of travel. For instance, thefirst mode of use corresponds to the vehicle being in motion, so theaccumulated usage corresponds to total mileage.

Accumulating the first usage includes accumulating at least one of aduration and a distance of travel on the first road type.

Accumulating the first usage includes accumulating a distance of travelof the first road type according to a duration of the identified timeperiods and an average travel speed on the first road type.

The method further comprises using the determined characteristic toidentify time periods during which the vehicle is in each mode of use ofa plurality of modes of use including the first mode of use. Usage ofthe vehicle in each mode of the plurality of modes is accumulatedaccording to the identified time periods.

In some examples, the plurality of modes of use comprises travel on aplurality of road types, each mode of use corresponding to a differentroad type. The plurality or road types can include a highway road typeand/or an urban road type (e.g., a “side street” road type).

In another aspect, in general, a method for estimating motion of avehicle makes use of a first sensor signal acquired from a sensortraveling with the vehicle. The sensor signal includes a speed relatedcomponent whose characteristics depend on a traveling speed of thevehicle. The first sensor signal is processed to estimate at least onecharacteristic of the speed related component of the acquired sensorsignal. Stored data is accessed, the accessed stored data including dataassociating speed of the vehicle with value of the at least onecharacteristic of the speed related component. The accessed data and theestimated at least one characteristic are then used to estimate thetraveling speed of the vehicle.

Aspects can include one or more of the following features.

The sensor traveling with the vehicle comprises an accelerometer, whichmay be an accelerometer affixed to the vehicle or an accelerometer of apersonal electronic device traveling with but not affixed to thevehicle. In some examples, the sensor signal comprises amultidimensional sensor signal, each dimension corresponding to adifferent direction relative to the vehicle.

The sensor traveling with the vehicle comprises a microphone.

The sensor signal comprises a time series (e.g., representing a timesampled or continuous waveform).

The at least one characteristic comprises a spectral characteristic. Forexample, the spectral characteristic characterizes frequencies of one ormore energy peaks, a vibration frequency of a component of the vehicle(e.g., a tire vibration), and/or an energy distribution over frequency.

The at least one characteristic comprises a timing characteristic. Forexample, the timing characteristic comprises a periodicity timecharacteristic or an inter-event time characteristic (e.g., a timebetween a front wheel and a back wheel encountering a bump in the road).

The data associating speed of the vehicle with value of the at least onecharacteristic comprises data characterizing a statistical relationship.For example, the data comprises a data table with records, each recordassociating a speed of the vehicle with a value of the at least onecharacteristic. As another example, the data represents a linearrelationship between a frequency of an energy peak and a vehicle speed.As yet another example, the data represents an inverse relationshipbetween an inter-event time characteristic and a vehicle speed.

The method further includes determining and storing the data associatingspeed of the vehicle with the one or more characteristics. A secondsensor signal is acquired from a sensor (e.g., the same or a similarsensor used to acquire the first sensor signal) traveling with thevehicle and acquiring a vehicle speed signal. The second sensor signalis processed to estimate the at least one characteristic of the speedrelated component of the acquired sensor signal. The data associatingspeed of the vehicle with the at least one characteristic is determinedto represent an association of the acquired vehicle speed and theestimated at least one characteristic.

Repeated estimates of the at least one characteristic over time are usedto estimate speed over time, and the estimate of speed over time is usedto estimate a distance traveled by the vehicle.

A drift of an speed derived from an inertial sensor according to theestimate of the traveling speed. In some examples, the sensor travelingwith the vehicle used to acquire the first sensor signal is the same asthe inertial sensor for which the drift is corrected.

Aspects can have one or more of the following advantages.

The approach provides increased accuracy over odometer or speedometerbased approaches.

A device implementing the approach may be simpler, less expensive andmore power efficient than OBD-based devices. Furthermore, use of such adevice may be less error or failure prone than use of an OBD device.

The approach does not require ongoing use of a positioning system, forinstance use of GPS.

Power requirements for a device may be relatively low, for instance, asa consequence of not requiring GPS. In some implementations, the lowpower requirement can enable long-term powering by an internal batterywithout requiring integration with a vehicle's power system. Anadvantage of the self-powering is that the vehicle's battery cannot bedrained by the device.

Once installed, the device does not necessarily require userinteraction.

Calibration and communication aspects of the system may be provided by auser's smartphone, which can be linked to the vehicle-installed devicevia a low-power radio (e.g., Bluetooth) link, thereby avoiding the needto implement such aspects in the device itself. However, the user'ssmartphone is not required on an ongoing basis is such examples.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram representing a vehicle traveling on aroadway.

FIG. 2 is a graph of acceleration spectral density versus time.

FIG. 3 is a graph of velocity versus time.

FIG. 4 is a graph autocorrelation versus wheelbase.

FIG. 5 is a graph predicted distance versus actual distance traveled.

DETAILED DESCRIPTION

Referring to FIG. 1, a vehicle 110 is represented as traveling along theroad surface 160 of a roadway at a velocity v. A tag 122 is affixed tothe vehicle, for example attached to the inside of the windshield asillustrated in FIG. 1. In this embodiment, the sensor 122 is abattery-powered sensor that includes one or more accelerometers, amicroprocessor, a memory, a real-time clock, and a wireless transceiver.In some embodiments, the sensor 122 is a “tag” as described inco-pending US application “System and Method for Obtaining VehicleTelematics Data,” Ser. No. 14/529,812, published as US2015/0312655A1,which is incorporated herein by reference.

Note that although the description below focuses on use of a single tag122 in a vehicle, in alternative embodiments there may be multiple tagson the vehicle, for example, with one tag affixed near the left side ofthe car and another tag affixed near the right side of the car.Furthermore, in yet other embodiments, the tag may not be permanentlyaffixed to the vehicle or the function of the tag may be incorporatedinto the communication device (described below).

A communication device 124, in this embodiment a cellular “smartphone”is, from time to time, in wireless communication with the tag 122 (e.g.,using a low power Bluetooth protocol), whereby it is configured toreceive vibration data from the tag, either as they are acquired by theaccelerometer(s) or as a batch after they have been acquired and storedin the memory of the tag. Note that the communication device 124illustrated in FIG. 1 is not required to be present at all times becausethe tag 122 can operate autonomously collecting data for later transferto the communication device 124 when it is present and communicativelycoupled to the tag. The communication device 124 optionally receivessignals from a positioning system allowing it to determine itsgeographic location. In this embodiment, the device 124 includes aGlobal Positioning System (GPS) receiver that receives and processeslocalization signals emitted from satellites 130 of the positioningsystem. The device 124 also includes a bidirectional wireless datacommunication transceiver, in this embodiment, that uses a cellulartelephone infrastructure. The device uses this transceiver to exchangedata with a remote server 150, which includes a processor 152 as well asa data storage subsystem 154.

Generally, as a supplement or a replacement to the tracking capabilitiesof the communication device 124 (e.g., smartphone) itself, the tag 122effectively also enables tracking capabilities. For example, when thecommunication device is present in the vehicle, its positioning systemcan be used to determine distance traveled, and when the communicationdevice is not in the vehicle, the tag can log sufficient data todetermine or estimate the distance traveled by the vehicle. Even whenthe communication device is in the vehicle, it may be preferable to logdata with the tag rather than use the communication device's positioningcapabilities in order to reduce power consumption by the communicationdevice. For instance, a GPS receiver of a smartphone may be turned onfrom time to time (e.g., every 10 minutes) and the positioninginformation may be used in combination with the data logged by the tagin order to determine the distance and/or speed traveled by the vehicle.In some embodiments, map data is also used in combination with the tagand positioning data. Various embodiments of the tag 122 support one ormore of the following operating modes.

In one operating mode, the tag 122 supplements monitoring of vehicleusage using the smartphone 124 by sensing vibration (e.g., acousticvibration in sound in the environment of the tag sensed by a microphone,or by accelerometer-based sensing of vibration of the tag itself) todetermine when the times when the vehicle is traveling (i.e., in atravel mode as opposed to a stationary mode). These times are logged inthe tag, and transferred to the smartphone when the smartphone and tagare next in communication. The smartphone has information about thedriver's past travel patterns, for example, including average traveldistance per unit time (i.e., speed) for various times of day and daysof the week. The smartphone then estimates the distance traveled duringthe times that the tag sensed the traveling times but the smartphone wasnot tracking the usage, and augments the smartphone's tracked usageusing the information from the tag. Note that in some embodiments, rawor partially processed sensor signals are provided to the smartphone,which makes the determination of when the vehicle was traveling, whilein other embodiments, the decision of whether the vehicle was travelingis made within the tag, for example, using parameters that configure thetag in a manner for all drivers and cars, or using parameters providedby the smartphone such that the traveling versus not traveling decisionmay be particularly adapted to the specific driver and car associatedwith that tag.

In another operating mode, not only is a traveling versus not travelingdecision made based on the senses vibration by the tag 122, a road typebeing traveled on is also determined, for example, by classification ofthe sensed vibration signal into a predetermined set of road types(e.g., highway, dirt road, etc.). For example, the spectral shape (i.e.,the distribution of energy in the sensed signal over different vibrationfrequencies) may be used to classify the road type. In some embodiments,the smartphone uses different average travel distance per unit time fordifferent road types to more accurately estimate the distance traveled.For example, the average distance traveled on a highway per unit timemay the greater than the average distance traveled on a dirt road.

In another operating mode, the sensed vibration by the tag 122 is usedto estimate the vehicle speed, and thereby provide a way to estimate thedistance traveled directly. As discussed below, a spectral peakfrequency may be used to provide information about vehicle speed, forinstance, based on wheel vibration whose frequency is proportional towheel rotation speed and therefore proportional to vehicle speed.Similarly, instead of or in addition to using a peak frequency, andautocorrelation time may be used to provide information about speedbased on the wheelbase of the vehicle. For instance, the autocorrelationtime may be related to the time between a front wheel hitting a bump anda rear wheel hitting the same bump. In such an operating mode, the tagis able to provide more specific information regarding the distancetraveled while the smartphone was not tracking travel, thereby enablingmore accurate augmentation of the smartphone collected data.

In yet another operating mode, the tag includes a multi-axisaccelerometer, and uses the accelerometer to infer distance traveled(e.g., by integration of a longitudinal component of the accelerationsignal). In some such embodiments, the sensed vibration by the tag isalso used to infer a vehicle speed. This inferred vehicle speed is thenused to correct or compensate for drift in the accelerometer signal.

Note that in the description below, generally, the tag is describedseparate from the communication device. In alternative embodiments,function of the tag and the communication device are combined, forexample, using built-in accelerometers in the communication device.Furthermore, the tag or communication device that houses theaccelerometers does not necessarily have to be firmly attached to thevehicle (e.g., in a user's pocket, in the glove-box, etc.) and theorientation of the accelerometers may inferred, for example ifnecessary, using the techniques described in U.S. Pat. No. 9,228,836,“Inference of Vehicular Trajectory Characteristics with Personal MobileDevices.” Note that in a number of embodiments described, theorientation of the tag is not important because the orientation does notnegate the ability to determine characteristics of the sensed signal,such as a spectral peak or an autocorrelation time.

In one or more embodiments, supporting one or more of the operatingmodes described above, when the vehicle 110 is in motion, the tag 122measures acceleration in one or more fixed directions relative to thevehicle's frame of reference (e.g., vertical, front-back, andside-to-side, or a rotation of these axes resulting from the orientationin which the tag is attached to the vehicle). As may be appreciated by aperson with experience driving or being a passenger in a moving vehicle,the nature of a vehicle's vibration may change as the vehicle changesspeed or as the nature of the road surface changes, and certain aspectsof a vehicle's vibration has speed-dependent timing, for instance, asthe vehicle's tires successively hit a pothole in the road surface. Moregenerally, there are a number of factors that affect the timing,spectral content, and/or direction of vibration of a vehicle as ittravels. These features of a vehicle's vibration provide informationrelated to the vehicle's speed and the type of road surface on which thevehicle is traveling.

One aspect of vibration is related to the rotation speed of thevehicle's wheels, represented as r (revolutions per minute, rpm). Somemechanical characteristics of the vehicle that may cause such vibrationinclude a faulty wheel alignment, poor tire balancing, and a tireimperfection.

Also, engine vibration may relate to engine speed, which depending onthe gear ratio, depends on vehicle speed. Some vibration may bepredominantly lateral (e.g., in some cases of faulty wheel alignment ortire imbalance), while some vibration may be largely vertical (e.g., insome cases of tire imperfection). Generally, the directionalcharacteristic of such vibration does not change over time. Referring toFIG. 2, spectral density as a function of time is shown with higherenergy being shown with increasing darkness in the figure. Referring toFIG. 3, actual vehicle speed as a function of time is shown on the sametime axis as in FIG. 2. It can be seen in these figures that the actualvehicle speed tracks the spectral peak. This tracking can be understoodby recognizing that a spectral peak at a frequency f (in Hertz)generally corresponds to a rotation speed of r=f×60/k, where k≥1 is aninteger related to the harmonic associated with the peak.

Yet another aspect of vibration, or more generally, an aspect of apattern of acceleration signals, relates to successive contact betweenthe front and then the back wheels of the vehicle and aspects (e.g,imperfections) of the road surface. As one example, as the vehicletravels along a road surface that has lateral expansion joints (e.g., asone may find on a bridge), the tag 122 will sense a vibration pattern asfront wheels hit a joint and then shortly after as the rear wheel hitthe same joint. If the front and rear wheels are separated by a distanced on the vehicle, and the vehicle is traveling at a speed υ, then onewould expect that the vibration events associated with the front andthen the rear wheels would be separated in time by a duration τ=d/υ. Theacceleration of the sensor includes components of the front wheel andthe rear wheel accelerations. Therefore, when the vehicle is travelingat a fixed speed υ, an autocorrelation of the acceleration signal showsa peak at delay τ. During the calibration of the system, the wheelbase dcan be estimated based on a known speed υ as d=τ×υ. In practice, theapproach to estimating the wheelbase takes into account the impulseresponse c(t) of the vehicle, for example, related to ringing ofsuspension. Correlation between an acceleration signal a(t) anda(t−d/υ))−c(d/υ)a(t) is evaluated for a range of different wheelbases d.The result is shown in FIG. 4, indicating that the estimated wheelbaseis approximately d=2.55 m, which matches the true wheelbase of d=2.51 mquite accurately. Later during use, a peak of an autocorrelation at atime τ allows the system to infer that the vehicle is traveling at aspeed υ=d/τ. This inference is performed in a set of time windows inwhich the vehicle speed is assumed constant for this computation.

Another aspect of vibration relates to speed and the smoothness of theroad surface. For example, travel on a gravel road will cause differentvibration characteristics than travel on a concrete surfaced highway.Therefore, some aspects of vibration can be used to determine when thevehicle is stopped versus in motion, and changes in engine or suspensionvibration frequency or amplitude can indicate the speed that the vehicleis traveling.

As introduced above, the system can operate in a calibration phase aswell as in an ongoing monitoring phase. Generally the calibration phaseinvolves “learning” a relationship between the acquired vibration signaland vehicle motion, and the “monitoring” phase uses this relationship toestimate or otherwise infer how the vehicle has traveled.

In the calibration phase, generally, the tag acquires data generally inthe same manner as it will during later monitoring. In the calibrationphase, the vehicle's usage is also determined according to a secondarymeans. In this embodiment, the smartphone uses its GPS receiver todetermine the vehicles motion, in particular, tracking its speed overtime. In some examples, the smartphone also determines othercharacteristics of the travel, for example the road type being traveledon over time based on map information available to the smartphone basedon built-in maps or information provided from a server over the datalink. A relationship, for instance a statistically estimated model,between the tag-acquired data and the smartphone-determined data is thendetermined, for example, using a process executed in the smartphone, oralternatively on a server remote from the smartphone that receives boththe tag-acquired and smartphone-determined data.

More specifically, in an example of the calibration phase, the sensortag measures and transmits complete 3-axis acceleration data to thesmartphone. The smartphone simultaneously determines speed from GPSmeasurements. All of these measurements are uploaded to the server. Theserver then breaks the acceleration data into short windows and performsa short-time Fourier transform on each window to compute thespectrogram. There are typically several spectral components in whichthe frequency of the largest peak in the spectrogram (in some range)varies proportionally to speed. Given this data, the relationshipbetween the features (e.g. spectral peaks) and the speed is determined.Also, the features that do not provide information about speed are alsodetermined so that they can be ignored. For example, certain lowfrequency ranges might be dominated by irrelevant information, forinstance motion from a windshield wiper. The matched data allows us todetect and reject these spurious signals.

Note that since the sensor tag is in a fixed orientation relative to thevehicle, it is possible to determine this orientation and store it onthe server. In the 3-axis case, the signals provide by the sensor arenot necessarily aligned with standard directions such as vertical,front-back, and side-to-side. However, in the calibration phase, theserver can determine a rotation of the data that yields data in thestandard directions. Therefore, if there is more information in oneparticular direction (e.g. vertically), the server can exploit this toimprove the speed estimate. The unrotated axes of the sensor tag alsoconvey useful information, as they capture vibration perpendicular tothe surface on which the sensor tag is mounted

In the monitoring phase, in general, the smartphone is absent from thevehicle, or at least not necessarily in communication with the tag ortracking travel of the vehicle. The tag collects the sensor data overtime. When the smartphone is next present, the stored data in the tag isuploaded to the smartphone, and from the smartphone to the server. Theserver then processes the uploaded data and uses the relationshipbetween acceleration data and speed and road type to estimate thedistance traveled in total and broken down by road type.

Because the acceleration data may be too large to easily store on thetag or transmit to the phone, the sensor tag in some embodimentsperforms a data reduction prior to transfer to the smartphone and insome embodiments prior to storage in the tag's memory. One such datareduction includes computing a short-time Fourier Transform (FT) on eachaxis of acceleration, and locating the largest peaks in a particularfrequency range. The tag stores the index (e.g., the number of thefrequency bin of the FT) and relative magnitude of the largest frequencypeaks. In an alternative data reduction, the tag uses a dynamic filterto estimate the principal spectral content. By storing only the locationof the spectral peaks, the data storage requirement is greatly reduced.When the smartphone is next present, these peaks are transmitted to thesmartphone, which in turn transmits them to the server. The server usesthe stored orientation to rotate the acceleration into the desiredreference frame.

The description above focuses on determination of vehicle speed. On theother hand, the total mileage is more important for some applications.There are several alternative methods of estimating mileage. First, thespeed estimates can be accumulated over time as an approximation of anintegral of speed being the distance traveled. Errors in speed maycancel, producing an estimate of mileage more accurate than theconstituent speed estimates.

The approaches described above were evaluated in experimental use in anumber of vehicles. FIG. 5 shows predicted distance traveled versus thetrue distance traveled, with each point representing one trip, forapproximately 5,000 vehicles, each taking at least 80 trips. The PearsonR value is 1.00 (to two significant digits), showing that the predictedapproach provides a highly accurate estimate.

In another embodiment, the tag detects when the car is in motion bymeasuring when vibration levels exceed some threshold. Multiplying theduration of time that the car is in motion by the mean speed of atypical driver, the tag estimates mileage. This method requires nolearning step (other than a priori knowledge of the mean speed of atypical driver). Also, the data bandwidth and memory requirements areextremely small; only the total time that the vehicle is in motion needsto be transmitted, not the underlying acceleration measurements. Theaccuracy of the mean speed can be improved by using collateralinformation, such as the driver's age.

In another embodiment, the tag is configured with the mean speed of aparticular driver and multiplies this speed by the time the vehicle isin motion to yield a distance estimate. This approach requires alearning step for each driver, but might produce more accurate estimatesthan using a global mean driver speed. If the end user wants anestimated mileage before the learning step is complete, a global meanspeed or a combination of a global and individualized mean speed can besubstituted in the interim.

In another embodiment, the tag measures how frequently and for how longthe vehicle stops or makes sharp turns. Vehicles on surface streets makefrequent stops and sharp turns; moreover, the mean speed is relativelylow. On the other hand, vehicles on highways make few stops or sharpturns and their mean speed is relatively high. The method can thenproduce a more accurate estimate of mileage by conditioning on vehicularstops and turns. The mean speeds can either be defined a priori based ontypical driving behavior, or learned per driver.

In another embodiment, the tag keeps track of the time of day when thevehicle is driven. The method can then distinguish mileage during theday from mileage at night.

In another embodiment, the tag can keep track of the type of road (e.g.,highway versus surface street) and distinguish mileage on each roadtype. This may be accomplished by configuring the tag with sensor signalcharacteristics associated with different road types, for example,provided via the smartphone after a calibration phase.

In some embodiments, the data acquired by the tag is used to infertravel along the road network. For instance, particular road segmentsmay have characteristic sensor profiles (e.g., distinctive accelerationbehavior due to the road surface). In a calibration phase, thesecharacteristics may be match with smartphone-determined trajectoryinformation. These profiles may be used as landmarks that are used toestimate distance traveled. In some embodiments, the characteristics maybe used to match map data to infer a route traveled by the vehicle. Suchmatching of sensor data to a map may be augmented by using lateral andfront-to-back acceleration data collected by the tag. Note that ingeneral, the server will typically observe data from multiple vehicles.Therefore, these acceleration landmarks can be shared between vehicles,so mileage can be estimated even if a vehicle is driving somewhere ithas never been before.

In some embodiments, the tag-acquired data may be compared (e.g., on thetag, the smartphone, or server) with data acquired on previous trips bythe same vehicle. Because vehicles often travel the same route, some ofthe trips along that route may have accurate mileage estimates from thesmartphone, and the acceleration features can detect matchedtrajectories, the method can produce much more accurate mileageestimates for those trips.

As introduced above, the tag may require relatively little power.Nevertheless, power requirements may be further reduced by cyclingoperation in a duty cycle, for example, in a 10% duty cycle in whichdata is recorded for 1 second, then the tag sleeps for 9 seconds. Inthat example, the total data transmission, power and memory requirementsreduce by a factor of 10, but the accuracy of the mileage estimation mayremain comparable to the results obtained from collecting all data.

The description above focuses on vibration measurements by a sensor tag.However, other measurements may be use in addition to or rather thanvibration. For instance, the sensor signal may represent the strength ofthe earth's magnetic field, which may vary as the vehicle travels on theroad network. Furthermore, sensing an acoustic signal may representvibration, including speed-related phenomena such as turbulent windnoise, engine vibration and the road surface.

In some embodiments, the tag uses an accelerometer, gyroscope, or otherinertial sensor to integrate distance traveled. In order to addressdrift of such integrated quantities, the tag uses characteristics suchas spectral content, timing of successive events, etc., to correct thedrift.

In a number of embodiments described above, the tag communicates with acommunication device, such as a smartphone, using a wireless channel,such as Bluetooth. It should be understood that in alternativeembodiments, the tag may communicate with other devices, such as acomputer via a Wi-Fi signal. In some embodiments, the tag maycommunicate in a manner similar to toll tags (e.g., EZpass tags) inresponse to a radio interrogation signal. Therefore, the user'scommunication device should not be considered to be essential or theonly way that the sensed information in the tag is used.

A variety of way of representing the relationship between the vibrationdata and the vehicle's speed, road type, etc. may be used. In someembodiments, this learning step uses a machine learning technique toproduce a small set of nonlinear or linear features indicative of speedor mileage. For example, the learning step trains an artificial neuralnet (ANN) to predict speed given acceleration data. Once trained in thecalibration phase, the ANN can be used at a server to process uploadeddata, or the parameters of the ANN may be downloaded to the tag orsmartphone, which implements the ANN and uploads the output of the ANN,which is then used to determine the mileage estimate. In someembodiments, the ANN may contain a middle layer of reduced size, therebyimplementing a data reduction. The tag can then compute the firstsection of the neural net and store the intermediate values from themiddle layer. The smartphone can receive the intermediate values andevaluate the second half of the neural net, or upload the server andhave the server evaluate the second half of the neural net. Thisapproach would reduce memory and transmission requirements on the tag.Of course, the ANN approach is only one example. Other statisticalapproaches, for example, based on regression or probabilistic models canbe used to represent the relationship between the data acquired orprovided from the tag and the characteristics of the vehicle's travelincluding speed and road type.

Implementation of the approaches described above may implement the dataprocessing steps (e.g., data storage, data reduction, and datacommunication) using hardware, software, or a combination of hardwareand software. The hardware can include application specific integratedcircuits (ASICS). The software can instructions stored on anon-transitory medium (e.g., non-volative semiconductor memory) forcausing one or more processors in the tag, the smartphone, and/or theserver, to perform the procedures described above.

1. A method for determining usage of a vehicle, the method comprising:acquiring a vibration signal from a sensor of a device affixed to thevehicle; processing the vibration signal in the device to determine acharacteristic of the signal related to travel by the vehicle; using thedetermined characteristic to identify a first mode of travelcorresponding to at least one of a first speed or range of speed and afirst type of road; accumulating usage in a data storage in the device,including accumulating a first usage for the vehicle during time periodsin which the vehicle is in the first mode of travel; and transmittingthe accumulated usage from the device.
 2. The method of claim 1 whereinthe sensor of the device affixed the vehicle comprises an accelerometer.3. The method of claim 2 wherein the vibration signal comprises amultidimensional signal, each dimension of the multidimensional signalcorresponding to a different direction relative to the vehicle.
 4. Themethod of claim 1 wherein the sensor of the device affixed to thevehicle comprises a microphone.
 5. The method of claim 1 wherein thefirst mode of travel corresponds to travel on a first road type.
 6. Themethod of claim 1 wherein the first mode of travel corresponds to travelat a first speed or range of speed.
 7. The method of claim 1 whereinaccumulating the first usage includes accumulating at least one of aduration and a distance of travel in the first mode of travel.
 8. Themethod of claim 7 wherein accumulating the first usage includesaccumulating a distance of travel of a first road type according to aduration of the identified time periods and an average travel speed onthe first road type.
 9. The method of claim 1 comprising: using thedetermined characteristic to identify time periods during which thevehicle is in each mode of travel of a plurality of modes of travelincluding the first mode of travel; and accumulating usage of thevehicle in each mode of the plurality of modes according to theidentified time periods.
 10. The method of claim 9 wherein the pluralityof modes of use comprises a travel on a plurality of road types, eachmode of travel corresponding to a different road type.
 11. The method ofclaim 10 wherein the plurality or road types includes at least one or ahighway road type and an urban road type.
 12. The method of claim 1wherein the sensor signal comprises a time series.
 13. The method ofclaim 1 wherein the characteristic comprises a spectral characteristic.14. The method of claim 13 wherein the spectral characteristiccharacterizes frequencies of one or more energy peaks.
 15. The method ofclaim 14 wherein the spectral characteristic characterizes a vibrationfrequency of a component of the vehicle.
 16. The method of claim 13wherein the spectral characteristic characterizes an energy distributionover frequency.
 17. The method of claim 1 wherein the characteristiccomprises a nonlinear or linear characteristic.
 18. The method of claim1 wherein the characteristic comprises a timing characteristic.
 19. Themethod of claim 18 wherein timing characteristic comprises a periodicitytime characteristic.
 20. The method of claim 18 wherein timingcharacteristic comprises an inter-event time characteristic.
 21. Themethod of claim 1 wherein the data associating speed of the vehicle withvalue of the at least one characteristic comprises data characterizing astatistical relationship.
 22. The method of claim 1 wherein the dataassociating speed of the vehicle with value of the at least onecharacteristic comprises a data table with records, each recordassociating a speed of the vehicle with a value of the at least onecharacteristic.
 23. The method of claim 1 wherein the data associatingspeed of the vehicle with value of the at least one characteristiccomprises data representing a linear relationship between a frequency ofan energy peak and a vehicle speed.
 24. The method of claim 1 whereinthe data associating speed of the vehicle with value of the at least onecharacteristic comprises data representing an inverse relationshipbetween an inter-event time characteristic and a vehicle speed.
 25. Themethod of claim 1 further comprising determining and storing the dataassociating speed of the vehicle with the one or more characteristics,the determining comprising: acquiring a second sensor signal from asensor traveling with the vehicle and acquiring a vehicle speed signal;processing the second sensor signal to estimate the at least onecharacteristic of the speed related component of the acquired sensorsignal; and determining the data associating speed of the vehicle withthe at least one characteristic to represent an association of theacquired vehicle speed and the estimated at least one characteristic.26. The method of claim 1 further comprising repeating use of estimatesof the characteristic over time to estimate speed over time, andcombining the estimate of speed over time to estimate a distancetraveled by the vehicle.
 27. The method of claim 1 further comprisingcorrecting a drift in an output of an inertial sensor according to theaccumulated usage.
 28. The method of claim 27 wherein the sensortraveling with the vehicle is the inertial sensor.
 29. The method ofclaim 1 wherein transmitting the accumulated usage from the devicecomprises transmitting the accumulated usage to a personal communicationdevice.
 30. The method of claim 29 further comprising augmenting usageinformation stored in the personal communication device with theaccumulated usage transmitted from the device.
 31. The method of claim 1further comprising using the information transmitted from the device toassess driving risk associated with the vehicle.
 32. The method of claim31 wherein assessing the driving risk associated with the vehicle isassociated with assessing an insurance risk.
 33. (canceled)
 34. Softwarestored on a non-transitory machine readable medium, the softwarecomprising instructions for causing one or more processors to: acquire avibration signal from a sensor of a device traveling with the vehicle;process the vibration signal in the device to determine a characteristicof the signal related to a use of the vehicle; use the determinedcharacteristic to identify a first mode of travel corresponding to atleast one of a first speed or range of speed and a first type of road;accumulate usage in a data storage in the device, including accumulatinga first usage for the vehicle during time periods in which the vehicleis in the first mode of travel; and transmit the accumulated usage fromthe device.
 35. (canceled)
 36. A device for affixing to a vehicle, thedevice comprising: a vibration sensor for providing a vibration signal;a signal processor configured to processes the vibration signal in thedevice to determine a characteristic of use of the vehicle; a usage modedetector configured to process the determined characteristic and providean indicator or whether the vehicle is in a first mode of travelcorresponding to at least one of a first speed or range of speed and afirst type of road; a data storage for accumulating a usage for thevehicle according to time periods determined from the providedindicator; and a transmitter for transmitting the accumulated usage fromthe device.
 37. (canceled)